Compositional brain scores capture Alzheimer's disease-specific structural brain patterns along the disease continuum

Area of Science:

• Neuroimaging
• Alzheimer's Disease Research
• Genetics

Background:

• Traditional multivariate methods often fail to capture the interdependent relationships between brain features in neuroimaging studies.
• Alzheimer's disease (AD) and genetic factors significantly influence brain structure along the disease continuum.

Purpose of the Study:

• To analyze relative brain volumetric patterns using compositional data analysis (CoDA) to understand AD's impact on brain structure.
• To investigate how genetic risk factors influence brain morphology across different stages of AD.

Main Methods:

• Utilized data from the Alzheimer's and Families (ALFA) and Alzheimer's Disease Neuroimaging Initiative (ADNI) studies.
• Applied CoDA to examine relative brain volumetric variations linked to AD stages and genetic risk.
• Compared AD progression groups with cognitively unimpaired amyloid-β-negative (CU A-) individuals.

Main Results:

• Identified disease stage-specific compositional brain scores differentiating CU A- individuals from those in later AD stages.
• Revealed a broader genetic landscape influencing AD brain morphology beyond the APOE ε4 allele.
• Demonstrated CoDA's ability to integrate data from multiple cohorts without stringent harmonization.

Conclusions:

• CoDA offers a novel multivariate framework for understanding AD-related structural brain changes.
• This approach can identify individuals at higher genetic risk for targeted interventions.
• CoDA enhances the reliability of cross-study comparisons in neuroimaging research.